Abstract

This paper develops a nonlinear statistical method that uses satellite radiance observations directly to estimate the downward longwave radiation (DLR) at the earth's surface, a necessary component of the surface energy budget. The proposed technique has rms regression errors of about 9 W m^–2 for clear-sky conditions, and about 4 to 8 W m^–2 for overcast conditions, depending on the cloud levels. It is shown that this technique can produce unbiased estimates over a large range of meteorological conditions, which is crucial for climate studies.

Sensitivity studies show that the DLR is most sensitive to errors in the cloud amount on average. Overall, the combined errors for an instantaneous DLR estimate, excluding the effects of the surface pressure errors, range from about 7 to 12 W m^–2 when there is a ±10% uncertainty in cloud amount and a ±100 hPa uncertainty in cloud-base pressure. When the cloud amount uncertainty rises to 30%, the combined DLR error ranges from about 10 to 25 W m^–2.

This clear-sky DLR estimation technique was validated preliminarily by using simulated radiation data. The DLR differences between estimated and calculated values have a standard deviation of about 9 W m^–2 and are unbiased in most conditions.

The validity of the DLR estimation technique has been demonstrated; however, validation for cloudy conditions, comparison with surface observations, and improvements related to surface pressure dependence and skin temperature discontinuity are left for future study.

Abstract

The Advanced Very High Resolution Radiometer (AVHRR) outgoing longwave radiation (OLR) product, which NOAA has been operationally generating since 1979, is a very long data record that has been used in many applications, yet past studies have shown its limitations and several algorithm-related deficiencies. Ellingson et al. have developed the multispectral algorithm that largely improved the accuracy of the narrowband-estimated OLR as well as eliminated the problems in AVHRR. NOAA has been generating High Resolution Infrared Radiation Sounder (HIRS) OLR operationally since September 1998. In recognition of the need for a continuous and long OLR data record that would be consistent with the earth radiation budget broadband measurements in the National Polar-orbiting Operational Environmental Satellite System (NPOESS) era, and to provide a climate data record for global change studies, a vigorous reprocessing of the HIRS radiance for OLR derivation is necessary.

This paper describes the development of the new HIRS OLR climate dataset. The HIRS level 1b data from the entire Television and Infrared Observation Satellite N-series (TIROS-N) satellites have been assembled. A new radiance calibration procedure was applied to obtain more accurate and consistent HIRS radiance measurements. The regression coefficients of the HIRS OLR algorithm for all satellites were rederived from calculations using an improved radiative transfer model. Intersatellite calibrations were performed to remove possible discontinuity in the HIRS OLR product from different satellites. A set of global monthly diurnal models was constructed consistent with the HIRS OLR retrievals to reduce the temporal sampling errors and to alleviate an orbital-drift-induced artificial trend. These steps significantly improved the accuracy, continuity, and uniformity of the HIRS monthly mean OLR time series. As a result, the HIRS OLR shows a comparable stability as in the Earth Radiation Budget Satellite (ERBS) nonscanner OLR measurements.

HIRS OLR has superb agreement with the broadband observations from Earth Radiation Budget Experiment (ERBE) and Clouds and the Earth’s Radiant Energy System (CERES) in the ENSO-monitoring regions. It shows compatible ENSO-monitoring capability with the AVHRR OLR. Globally, HIRS OLR agrees with CERES with an accuracy to within 2 W m⁻² and a precision of about 4 W m⁻². The correlation coefficient between HIRS and CERES global monthly mean is 0.997. Regionally, HIRS OLR agrees with CERES to within 3 W m⁻² with precisions better than 3 W m⁻² in most places. HIRS OLR could be used for constructing climatology for applications that plan to use NPOESS ERBS and previously used AVHRR OLR observations. The HIRS monthly mean OLR data have high accuracy and precision with respect to the broadband observations of ERBE and CERES. It can be used as an independent validation data source. The uniformity and continuity of HIRS OLR time series suggest that it could be used as a reliable transfer reference for the discontinuous broadband measurements from ERBE, CERES, and ERBS.

Abstract

Simultaneous observations by the Earth Radiation Budget Experiment (ERBE) scanning radiometer and the High-Resolution Infrared Sounder (HIRS) on board the NOAA-9 spacecraft have been used to validate a multispectral technique for estimating the outgoing longwave radiation (OLR) from the earth-atmosphere system. Results farm approximately 100 000 collocated observations show that the HIRS technique provides instantaneous OLR estimates that agree with the ERBE observations just as well as different ERBE scanners agree with each other—about 5 W m⁻² rms. Although there are differences between the HIRS and ERBE estimates that depend upon the scene type and time of day, the HIRS technique explained more than 99% of the variance of the ERBE observations for both day and night observations. The results suggest that the HIRS OLR technique is a suitable replacement for the Advanced Very High Resolution Radiometer technique now used by the National Oceanic and Atmospheric Administration for operational estimates of the OLR.

Abstract

A new technique for estimating outgoing longwave radiation from observations on the NOAA operational satellites has been developed from a regression analysis of radiation model calculations. The technique consists of a weighted sum of radiance in but four intervals sensed by the High-resolution InfraRed Sounder (HIRS). The analysis shows that model outgoing flux may be reproduced to within ±2 W · m⁻² rms, which is about a factor of 4 smaller than the rms error of the method used by NOAA to estimate flux from the AVHRR. The small errors suggest that the new technique holds the promise of eliminating the large systematic errors possible with the current NOAA technique. Additionally, the new technique often the possibility of directly relating flux changes to changes in atmospheric parameters.

The National Weather Service (NWS), in collaboration with the Environmental Protection Agency (EPA), now issues an Ultraviolet (UV) index forecast. The UV index (UVI) is a mechanism by which the American public is forewarned of the next day's noontime intensity of UV radiation at locations within the United States. The EPA's role in this effort is to alert the public of the dangerous health effects of overexposure to, and the accumulative effects of, UV radiation. The EPA also provides ground-level monitoring data for use in ongoing verification of the UVI. The NWS estimates the UVI using existing atmospheric measurements, forecasts, and an advanced radiative transfer model. This paper discusses the justification for a forecasted index, the nature of UV radiation, the methodology of producing the UVI, and results from verifying the UVI. Since the UVI is an evolving product, a short discussion of necessary improvements and/or refinements is included at the end of this article.

Geostationary satellites have provided routine, high temporal resolution Earth observations since the 1970s. Despite the long period of record, use of these data in climate studies has been limited for numerous reasons, among them that no central archive of geostationary data for all international satellites exists, full temporal and spatial resolution data are voluminous, and diverse calibration and navigation formats encumber the uniform processing needed for multisatellite climate studies. The International Satellite Cloud Climatology Project (ISCCP) set the stage for overcoming these issues by archiving a subset of the full-resolution geostationary data at ~10-km resolution at 3-hourly intervals since 1983. Recent efforts at NOAA's National Climatic Data Center to provide convenient access to these data include remapping the data to a standard map projection, recalibrating the data to optimize temporal homogeneity, extending the record of observations back to 1980, and reformatting the data for broad public distribution. The Gridded Satellite (GridSat) dataset includes observations from the visible, infrared window, and infrared water vapor channels. Data are stored in Network Common Data Format (netCDF) using standards that permit a wide variety of tools and libraries to process the data quickly and easily. A novel data layering approach, together with appropriate satellite and file metadata, allows users to access GridSat data at varying levels of complexity based on their needs. The result is a climate data record already in use by the meteorological community. Examples include reanalysis of tropical cyclones, studies of global precipitation, and detection and tracking of the intertropical convergence zone.